Industrial IoT is not a new concept; in fact, it has been around for decades. It is currently defined as a blend between cyber and physical systems that incorporate connected machinery that, with the help of software and big data, can not only analyze and make decisions but are also predictive, using sensor data that comes from a wide array of dispersed sources. For years, many factories have been relying on smart sensors to monitor and control their production lines. Part of Industry 3.0, car and food factories for example have already been configured to be monitored by such sensors. A few years ago, this was an expensive privilege reserved for the few, today, in industry 4.0 these sensors have not only become smarter, but cheaper. As a result, perfecting the processes in manufacturing has become easier than ever, and in turn the technology at our disposal also allows us to maximize production efficiency.

The technology itself is slowly moving towards self-sufficiency and reliance. Thinking of the inevitable emergence of artificial intelligence (AI), companies will no longer need to solely depend on the input of their employees, which will definitely contribute to reducing human error. Interestingly enough, companies are also looking to reduce machine downtime especially since we are approaching a point where everything is expected to be monitored by robots and where the sole purpose of the human resource would be to manufacture, but to manufacture more robots. With artificial intelligence, the robots, our very own creations, will thus be designed to do our jobs so much better than we can; an ironic fact of our day and age. Industrial IoT will eventually produce an indiscernible amount of big data which can be utilized to make AI a reality. The possibilities are endless, and predictive selfsufficient machinery are only one example. Analyzing this sum of data however won’t be easy, a task that even themost brilliant of humans cannot possible carry out. For that reason, it is detrimental that we enhance the speed and accuracy of big data analysis.

Moving on, Industry 4.0 is taking shape given three key characteristics. The first of those is interoperability which is defined as “the ability of computer systems or software to exchange and make use of information.” In other words, interoperability refers to the ability to integrate different systems together and work smoothly without human intervention. While companies are adopting hybrid systems, buying from different suppliers/ manufacturers/vendors to reduce cost, they are also going through a process of integration, to connect their equipment, and disintegration, to secure them. The second characteristic is decentralization, getting rid of those bulky centralized data centers, and instead creating an IoT network of connected devices. These, of course, come with a set of security challenges that can and will be addressed with the rise of powerful digital and cybersecurity giants across the world. The third characteristic is operating in real time, or the ability of devices and systems to seamlessly connect, communicate and react in real time based on the data they share.

In a nutshell then, industrial IoT is all about creating “intelligent cyber-physical systems” that can think and act for themselves, reducing human error, cost, wasted time, and ultimately do the job that we, the masterminds behind it all, could only dream of doing ourselves.